docstr: `
  get autodiff grads from numpy string expression
  tensors: numpy tensor vals
docstr_tok: ["`","get","autodiff","grads","from","numpy","string","expression","tensors",":","numpy","tensor","vals"]
code: def _get_numerical_grads_and_val(
      self, f_str, tensors, next_error=None
  ):
      """`
      get autodiff grads from numpy string expression
      tensors: numpy tensor vals
      """
      # buffer for gradients
      gradients = []
      for tensor in tensors:
          gradients.append(np.zeros(tensor.shape))
      # function values
      f_val = TestAutodiff._numpy_call(f_str, tensors)
      # init next error
      if next_error is None:
          next_error = np.ones_like(f_val)
      # numerical gradients
      for tensor, gradient in zip(tensors, gradients):
          gradient_flat = np.copy(gradient.reshape((-1,)))
          ind = 0
          for x in np.nditer(tensor, op_flags=["readwrite"]):
              # backup
              x_backup = np.copy(x)
              # increment
              x[...] = x + self.delta
              f_inc = np.sum(
                  TestAutodiff._numpy_call(f_str, tensors) * next_error
              )
              x[...] = x_backup
              # decrement
              x[...] = x - self.delta
              f_dec = np.sum(
                  TestAutodiff._numpy_call(f_str, tensors) * next_error
              )
              x[...] = x_backup
              # gradient
              gradient_flat[ind] = (f_inc - f_dec) / (2.0 * self.delta)
              ind += 1
          # write to gradient
          gradient[:] = gradient_flat.reshape(gradient.shape)
  
      return [gradients, f_val]
  
imports: from builtins import zip
  import numpy
  import pprint
  import pytest
  from neon import NervanaObject
  from neon.backends.autodiff import Autodiff
  from utils import allclose_with_out
label_desc: Use image node_id 5 for calling the TestAutodiff obj's underlying member method code with example usage: obj._get_numerical_grads_and_val(f_str, tensors, next_error) and returns: list